Analyzing Methods Of The Grass-roots Big Data Governance For Urban Public Safety

Grassroots society is the basic unit of urban system and the foundation of urban public safety.It usually includes streets,communities,residents and other related subjects(such as merchants,non-governmental organizations,non-governmental organizations,enterprises and institutions).China's urban public safety management system presents an inverted pyramid structure.There is a serious dislocation between the workload and supporting resources(including human resources,financial and material support)in the public safety management of grass-roots society.Strengthening the public safety management of grass-roots society has become a basic trend in the new era of deepening reform in China.As public safety services continue to sink to the grass-roots level,inefficient human work obviously can not meet the public safety management needs of the grass-roots society.Thus,the grass-roots government is collecting more and more grassroots social big data to improve the effects of public security services.Big data analysis has become a typical development trend.Various kinds of big data algorithm systems(such as infectious disease analysis,building fire prediction and waterlogging response)continue emerging.Grassroots social big data analysis relies heavily on data aggregation analysis,some of which come from grassroots government management departments,some from non-governmental organizations,and some from urban residents.Facing the organization,regulation and technical problems brought by multi-source big data aggregation,big data governance has become the global core problem.With the introduction of diversified big data items into public safety contexts,the data links become more and more complex.Grass roots big data governance(GBDG)has become a complex system engineering.Although existing studies have provided a wealth of big data governance decision-making strategy solutions from different levels,such as organizational incentives,policy regulation and technical support,they pay more attention to specific aspects of big data governance and lack systematic cognition and response to governance problems.In this context,the complex problem scenario response of GBDG needs more systematic and intelligent governance analysis methods.In this thesis,for the public safety contexts,the following aspects of GBDG analysis are carried out.First of all,focusing on 4 kinds of public safety contexts,this paper defines the concept of GBDG from the perspective of problem response.Based on the in-depth investigation of 18 district and county-level governments in China,the contradiction between the diversity of big data types and the complexity of big data governance in the context of public asfety is clarified;In theory,big data governance analysis is characterized as a complex and uncertain system with emergence.Based on the best practice theory and multi-layer theoretical method,the theoretical framework of big data governance analysis is constructed,including three linkage integration components: problem scenario recognition analysis,best practice analysis and driving way analysis.The research routes of each big data governance component are designed respectively.Secondly,the scenario analysis method of GBDG is proposed.The core of this method is to construct a meta model representing common domain knowledge by using the four-level meta-model framework,which is called scenario meta model in this thesis.Supported by a meta-model instantiation mechanism based on Antecedent-Behavior-Consequence(ABC)theory and big data governance operation framework,customized scenario modeling of diversified safety service contexts can be realized.Through the real investigation case of COVID-19 epidemic prevention and control and waterlogging analysis in Wuhan High-tech Zone,it is found that this method can provide key scenario information for big data governance problem identification,practice scheme design guidance and practice value recognition,so as to reduce the uncertainty of governance decision and improve the efficiency of decision-making.At the same time,the proposed method is proved to support low-cost and high flexibility scenario modeling in new application contexts.Thirdly,it puts forward the GBDG best practice analysis method.By matching governance problem scenarios,best practices help to achieve the optimal state description of big data governance under the target scenario,wh ich will provide a key basis for the generation of governance driving ways.Existing studies focus on the top-level guidance of best practice,which is difficult to adapt to the complex and diverse real problem scenarios.Based on this,the case drive n best practice method is elaborated: the practice list is generated by the scenario similarity algorithm based on the practice case;According to different problem scenarios,combined with the practice effect data of practice cases and the effect evaluation algorithm to reduce the practice list,the practice item combination driven by multiple cases is completed from the completeness of practice items and the time window of data analysis.Case driven method is helpful to solve the problems of best practice situation expression,unclear practice composition and poor objectivity of evaluation.Then,the driving way analysis method of GBDG is proposed.The core task of big data governance is to design,deploy and implement driving ways,that is,systematic solutions to driving governance problem solving.Guided by the best practice implementation,this paper proposes a driving way analysis method based on rule-embedded model integration: from organization,regulation and technology levels,the multi granularity driving task network is established respec tively;From the perspective of practice elements,based on set pair analysis theory,this paper evaluates the differences between best practice and target practice in terms of element composition;Based on the fuzzy belief rule base inference,the main driving task that needs to be adopted is identified;The model integration method is used to decompose the main task and correlate the meta task execution,so as to generate the executable driving way.Finally,the paper puts forward the method of starting p rocess mining of big data governance policy agenda.On the basis of building a big data governance policy support framework,this paper focuses on and solves the problem of small sample process mining of big data governance policy agenda.Specifically,thi s paper proposes a cross-policy-domain knowledge transfer method to starting process mining.Based on the empirical results of 10 policy areas in Wuhan High-tech Zone,it is shown that the proposed method achieves better process mining when the policy agenda start-up process only has a small sample set,and better deals with the problems of incomplete process and noisy data.The generated process model has high guiding value for the agenda start-up of big data governance policy.